Abstract: A method includes providing a Fibonacci confidence interval level on user program viewership data to distinguish user interest level in different linear entertainment programs. The method also includes creating a behavior shift feature space for acquiring information about user behavior over time based on a behavior sequence, a first derivative on the behavior sequence, and a second derivative on the behavior sequence. The method further includes utilizing, based on a trained machine learning model, a transformer structure and attention to map user data from the behavior shift feature space into a prediction of the Fibonacci confidence interval level.

Abstract: A method includes obtaining, based on a sequential graph-based model, gaming exposure information over time, where the gaming exposure information includes device-level preferences and household-level preferences. The method also includes combining one or more raw user behavior sessions into a gameplay session based on the obtained gaming exposure information. The method further includes providing a scoring metric to (i) check an extent of multi-matching in the obtained gaming exposure information and (ii) remove untrustworthy gaming exposures from the obtained gaming exposure information. In addition, the method includes generating, based on a feature engineering pipeline, one or more game segments running in a production environment, where the one or more game segments are identified for ancillary content based on inferences by a machine learning model trained using the gaming exposure information.

Abstract: In one embodiment, a method includes receiving user behavior data and contextual information associated with the user behavior data, the contextual information including a first data portion associated with a first context type. The method includes generating, from the user behavior data and the contextual information using a hashing algorithm, a first heterogeneous hash code including a first portion representing the user behavior data and a second hash code portion representing the first data portion associated with the first context type. The method includes accessing a second heterogeneous hash code including a third hash code portion representing a second data portion associated with the first context type. The method includes comparing the first heterogeneous hash code with the second heterogeneous hash code including determining similarity between the second hash code portion of the first heterogeneous hash code and the third hash code portion of the second heterogenous hash code.

Abstract: A method includes obtaining (i) device data associated with multiple electronic devices and (ii) advertisement data associated with one or more advertisement campaigns. The method also includes identifying first features and second features, where the first features correspond to the device data and the second features correspond to the advertisement data. The method further includes generating a graph relating usage history of the multiple electronic devices and one or more advertisement genres. The method also includes identifying a specified electronic device from among the multiple electronic devices and an advertisement segment from among the one or more advertisement campaigns using the first features, the second features, and the graph. In addition, the method includes providing the advertisement segment to the specified electronic device.

Abstract: In one embodiment, a computer-implemented method includes acquiring sequential user behavior data including one-dimensional data. The user behavior data is associated with a user. The method includes abstracting features from the sequential user behavior data to cover short-term and long-term timeframes. The method includes determining one or more properties of the user based on the features.

Abstract: An electronic device includes at least one processor configured to obtain user data associated with a plurality of devices from multiple data sources. The at least one processor is also configured to determine a static weight for each of the plurality of devices based on at least one source of the multiple data sources. The at least one processor is further configured to identify a portion of the plurality of devices that represents the plurality of devices based on the static weight and a dynamic weight. In addition, the at least one processor is configured to determine the dynamic weight for each of the portion of the plurality of devices while the portion of the plurality of devices is identified, where the dynamic weight is based on one or more sources of the multiple data sources.

Abstract: A method includes obtaining, using at least one processor of an electronic device, one or more instance level supervised artificial intelligence (AI) models. The method also includes obtaining, using the at least one processor, aggregated level label information related to the one or more instance level supervised AI models. The method further includes obtaining, using the at least one processor, instance level feature information related to the one or more instance level supervised AI models. In addition, the method includes training, using the at least one processor, the one or more instance level supervised AI models using the instance level feature information and the aggregated level label information to obtain one or more trained instance level supervised AI models.

Abstract: In one embodiment, a computer-implemented method includes acquiring sequential user behavior data including one-dimensional data. The user behavior data is associated with a user. The method includes abstracting features from the sequential user behavior data to cover short-term and long-term timeframes. The method includes determining one or more properties of the user based on the features.

Abstract: In one embodiment, a method includes receiving user behavior data and contextual information associated with the user behavior data, the contextual information including a first data portion associated with a first context type. The method includes generating, from the user behavior data and the contextual information using a hashing algorithm, a first heterogeneous hash code including a first portion representing the user behavior data and a second hash code portion representing the first data portion associated with the first context type. The method includes accessing a second heterogeneous hash code including a third hash code portion representing a second data portion associated with the first context type. The method includes comparing the first heterogeneous hash code with the second heterogeneous hash code including determining similarity between the second hash code portion of the first heterogeneous hash code and the third hash code portion of the second heterogenous hash code.

Abstract: A method and system for multi-user, multi-device content access metering and control is provided. In one embodiment, a system implements a method such that in response to user login requests, the system controls login access by providing multiple users login access to plural electronic devices capable of communicating via a communication system. Further, in response to user content access actions, the system controls access to content by selectively providing content to said one or more users via said one or more electronic devices based on content access policies.

Abstract: A system for measuring user engagement of content comprises an electronic device for playing a piece of content, at least one sensor for capturing data relating to environment context and user activity during playback of the piece of content, and an engagement measurement application for measuring user engagement of the piece of content. The engagement measurement application measures user engagement of the piece of content based on data captured by the sensors.

Abstract: A method is provided that includes downloading a full-app configured to display full-app content and obtain original context based on the full-app content. A first micro-app configured to run with the full-app content is downloaded. The middle partner intercepts the full-app content. The full-app content and the original context are augmented with additional context information.

Abstract: A method for tracking user behavior includes providing multiple frames from a source to an electronic device, automatically capturing one of the multiple frames viewed on a display device connected to the electronic device based on an event, matching a pattern of the captured frame with a frame template by searching a plurality of frame templates, using the matched frame template to extract information from the captured frame, and storing the extracted information for tracking user behavior.

Abstract: A method includes retrieving content playing or to be played by a full-app and/or a micro-app. Additional context information is determined for the content. The content is augmented with the additional context information. The augmented context information is sent to a first micro-app for use in retrieving additional content to play or display along with the content.

Abstract: A method for tracking user behavior includes providing multiple frames from a source to an electronic device, automatically capturing one of the multiple frames viewed on a display device connected to the electronic device based on an event, matching a pattern of the captured frame with a frame template by searching a plurality of frame templates, using the matched frame template to extract information from the captured frame, and storing the extracted information for tracking user behavior.

Abstract: A system for measuring user engagement of content comprises an electronic device for playing a piece of content, at least one sensor for capturing data relating to environment context and user activity during playback of the piece of content, and an engagement measurement application for measuring user engagement of the piece of content. The engagement measurement application measures user engagement of the piece of content based on data captured by the sensors.

Abstract: In a first embodiment of the present invention, a method is provided for enhancing full-app content playing on a first device, the method comprising: receiving a first selection of a first item from a first micro-app; receiving a second selection of a second item from a second micro-app, wherein the second micro-app is different than the first micro-app and the second selection is in a different format than the first selection; retrieving item context information regarding the first item; retrieving item context information regarding the second item; issuing a query to a micro-app partner based upon both the item context information regarding the first item and the item context information regarding the second item; receiving results from the micro-app partner; and causing at least a portion of the results to be displayed or played.

Abstract: An offsite user having a device can collaborate with an onsite user having an onsite device. The offsite device may be a handheld device, such as a smartphone and the onsite device is generally stationary. The onsite device receives a context from the offsite device wherein the offsite user is asking the onsite user for help with information or making a decision. The offsite device may not have the full range of services that the onsite device has and collaborates with the onsite device/user in making a decision. The onsite device receives the context, and obtains recommendations from appropriate Web services. The onsite user may view these recommendations and sends a reply message back to the offsite user telling her what she may do.

Abstract: A community of consumers of media content is created by providing users of an interactive television system with the capability to augment content and provide the augmented content to a repository. The augmented content may include rankings, votes, micro-games, user created tags, metadata, or other information associated with content. The augmented content is then made available to the community. Financial incentives may be provided to users to contribute augmented content, to rank augmented content, or to judge augmented content.

Abstract: In a first embodiment of the present invention, a method is provided comprising: displaying full-app content using a full-app on a first device, wherein the full-app is associated with a full-app partner and was downloaded from an app store; running a micro-app simultaneously with the full-app content playing on the first device, wherein the micro-app is associated with a micro-app partner; performing a revenue generating event on the first device; retrieving current context information; dynamically assigning a percentage of shared revenue associated with the revenue generating event to each of the app store, the full-app partner, and the micro-app partner, based upon the current context information; and causing revenue associated with the revenue generating event to be shared among the app store, the full-app partner, and the micro-app partner based on the dynamically assigned percentages.